The Importance of Models in Data Analysis with Small Human Movement Datasets - Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans

Abstract

Machine learning has shown impressive improvements recently, thanks especially to the results shown in deep learning applications. Besides important advancements in the theory, such improvements have been associated with an increment in the complexity of the used models (i.e. the numbers of neurons and connections in neural networks). Bigger models are possible given the amount of data used in the training process is increased accordingly. In medical applications, however, the size of datasets is often limited by the availability of human subjects and the effort required to perform human experiments. This position paper proposes the integration of bioinspired models with machine learning.